big data, big challenges - my.davenport.edu€¦ · “big data” is a hot topic these days, but...

1 © 2015 ELLUCIAN. CONFIDENTIAL & PROPRIETARY | BUGMI

Big Data, Big Challenges Moving Toward Business Analytics Maturity

Dr. Henry DeVries

Ellucian

September 25, 2015

BUGMI


Session Rules of Etiquette

Please turn off your cell phone/pager

If you must leave the session early, please do so as discreetly as

possible

Please avoid side conversation during the session

Thank you for your cooperation!


Introduction


About Me…

Henry DeVries

• Management Consultant, Principal

Targeted services for Business Analytics

• Ph.D. Cornell University

40 years in higher education

• Faculty member, administrator, researcher

o 16 years as CIO at Calvin College in Grand Rapids, MI

o 13 years (concurrently) as CFO

o Active in NACUBO, CACUBO, EDUCAUSE, CCCU


Introduction

“BIG Data” is a hot topic these days, but just what does it mean

for higher education?

We’ll explore just what big data is in the context of higher

education and learn how business analytics tools can help

address the challenges of big data.


1 Challenges of Big Data

2 Challenges Facing Higher Ed

3 Challenge’s to Reaching Business Analytics Maturity

4 Challenges to Big Data Projects

Agenda


Definitions

Is it Business Intelligence?

• “BI is any activity, tool, or process used to obtain the best information to

support the process of making decisions.”

-- BI for Dummies

• Purpose is to produce timely, accurate, high-value, actionable information.

Is it Business Analytics?

• “The discovery and communication of meaningful patterns in data”

o 1) descriptive and predictive models to gain knowledge from data

(data analysis).

o 2) using this insight to recommend action or guide decision-making

(communications)


EDUCAUSE --

“Analytics is used in a higher education environment to analyze

various collected data points to gain insight and make informed

decisions about complex issues.

Specific areas include academic analytics, business intelligence,

and learning analytics”

• Academic analytics: key institutional goals

• Business Intelligence: business performance

• Learning analytics: teaching/learning transaction


Challenges of BIG Data


Definition of Big Data

Big data is a

collection of data

sets so large and

complex that it

becomes difficult to

process using on-

hand database

management tools

or traditional data

processing

applications.

In 2012, Gartner

updated its “3V’s”

definition –

“Big data are

high volume,

high velocity,

and/or

high variety.

http://en.wikipedia.org/wiki/Data_set

http://en.wikipedia.org/wiki/Data_set


What Happens in a Internet Minute?


Just One Example…

1 engine,

in one hour,

20 Terabytes

1 Boeing 737,

NYC to LAX,

240 Terabytes


The Internet of Things


“The Industrial Internet”

Industrial Internet: Pushing the Boundaries of Minds and Machines, Evans and Annunziata, Nov 26, 2012


Powerful Motivation


Challenges facing Higher Ed “Our big data challenge”


Funding Pressures

Accountability

Macro Trends in Education


Resulting Higher Ed Challenges

• Optimizing Recruitment, Retention and Fundraising

• Increasing Online Course Capacity to Drive New Enrollment

• Improving Grant Writing Expertise, Industry Partnerships

Revenue Management

• Maximizing Use of Fixed Assets While Containing Cost

• Targeting Consolidation, Process Improvements, Metrics

• Financial visibility, discipline, controls

Operational Efficiency

• Reducing the Friction Associated with Pursuing Degree

• Applying Technology to Tailor Learning Process to Student

• Matching Interests, Aptitudes and Opportunities

Student Success

• Pressure to Demonstrate Institutional Effectiveness

• Focus on Retention, Graduation Rates, Learning Outcomes

• Many Constituencies to Serve Accountability

• Evaluating Effectiveness of Investments

• Using Technology to Gain Operational Efficiencies

• Still Want to Meet Expectations of “YouTube” Generation Technology

Revenue Management

Operational Efficiency

Student Success

Accountability

Technology


Challenges to Reaching

Business Analytics Maturity


Detailed

Data

Visibility, Insight, and Foresight:

Equipping People with the Information They Need to Be Successful

EXECUTIVES Dashboards & Scorecards for

Performance Management

“Am I achieving my goals?”

MANAGEMENT Dashboards, Reports and

Analytics to Monitor Progress

“How am I doing?”

KNOWLEDGE WORKERS Ad-hoc analysis tools to identify

and understand trends

“Why is this happening?”

STAFF Production reports and ad-hoc

access for daily operations

“What do I need to do?”

Trend,

Summary

Data

Performance

Data

Data Needs Audience


Logical Progression to the Questions that BA addresses

What happened?

Why did it happen?

What’s happening/trending now?

What do we think will happen?

What do we want to happen?

Descriptive analytics

Diagnostic analytics

Trend Analytics

Predictive Analytics

Prescriptive Analytics


BA Maturity in Higher Education

Data Sophistication & Institutional Value

Data-driven planning and forecasting

MONITORING WHAT is trending

NOW?

Ability to model, manage, and

adapt

OPTIMIZING What do we WANT

to happen?

Primarily batch and some ad hoc

reports

Increase in ad hoc

reporting and Information on demand

ANALYZING WHY

did it happen?

INFORMING WHAT

happened? Dashboards

for managers

and analysts

PREDICTING WHAT do we THINK

will happen?

Managed Reporting

Ad Hoc Reporting & Queries

Operational Dashboards

Scorecards, KPIs & Benchmarking

Predictive Analytics

Busin

ess A

naly

tics M

atu

rity

ODS

EDW

PM

ERP

EDW +

Statistical


What Else Happens with Evolving Business Analytics?

Data volume grows

Number of users grows

Depth of analysis grows

Query complexity grows

Need to visualize grows

Expectations grow

We hope that:

Data-driven decision-making grows


Challenges to Big Data Projects


Top Barriers to Effective BI (From Ellucian 2011 BI Research)

Lack of analytical drill-down capabilities

Lack of integrated data sets

Lack of departmental access to data

Slow turnaround time, IR/IT bottleneck

Lack of analytical staff


Current tools: tracking KPIs/reporting data (Ellucian/AIR 2014 Research)

60% - MS Excel

16% - Crystal Reports

16% - MS SharePoint

14% - IBM Cognos

11% - Evisions Argos


What are current tool barriers? (Ellucian/AIR 2014 Research)

39% - need more training

38% - cost

33% - the right data aren’t available

30% - inability to engage all institutional departments

29% - user friendliness

28% - lack of historical data and decision capture

24% - no ERP integration


The ideal analytical tool (Ellucian/AIR 2014 Research)

74% - access to all required data

69% - ability to capture all historical data and decisions

59% - ability to engage all major departments electronically

50% - out-of-box integration with ERP

48% - built-in KPIs and metrics for tracking performance


Cultural Issues “Lack of Cooperation…”

Engage IT and IR early, and often

Data must be reliable

Data must be shared, not held

Data must be trusted

Data definitions must be uniform across the institution

Data, and reporting, must be secure

“Single source of the truth”


Resource Issues

Make sure you understand the scope of the project

• Time

• People

• Dollars

Be prepared to make the investment in staff to use these tools

• At all levels, appropriate to the task


Human Capital Issues

Be prepared to train your people

It’s a hot market for qualified BI analysts; “brain drain”

Consider options for shared services

Leverage the culture of collaboration that is common in higher

education


Sustainability Issues

It will require additional resources over time

• IT/technical staff for central operations

• IT/IA/Reporting analysts to support departmental users with the new tools

Knowledge transfer to institutional employees

• Effective use of the new tools

• Understanding new policies and procedures

• How to leverage the new information environment

Change management will be an important aspect of the effort to

build this sustainability


Closing


Summary

Big data is coming, but higher ed has access to the tools to deal

with the challenges

Higher ed analytics, 3 domains (EDUCAUSE)

• Academic analytics: strategic

• Business intelligence: operational

• Learning analytics: student-focused

A mature business analytics deployment can help you manage

the big data challenge(s) in higher ed

Understand what you are getting yourself into


Questions & Answers


Thank you!

Dr. Henry DeVries

[email protected]

616-560-5477

big data, big challenges - my.davenport.edu€¦ · “big data” is a hot topic these days, but...

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